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cs/0005013
|
Practical Reasoning for Very Expressive Description Logics
|
cs.LO cs.AI
|
Description Logics (DLs) are a family of knowledge representation formalisms
mainly characterised by constructors to build complex concepts and roles from
atomic ones. Expressive role constructors are important in many applications,
but can be computationally problematical. We present an algorithm that decides
satisfiability of the DL ALC extended with transitive and inverse roles and
functional restrictions with respect to general concept inclusion axioms and
role hierarchies; early experiments indicate that this algorithm is well-suited
for implementation. Additionally, we show that ALC extended with just
transitive and inverse roles is still in PSPACE. We investigate the limits of
decidability for this family of DLs, showing that relaxing the constraints
placed on the kinds of roles used in number restrictions leads to the
undecidability of all inference problems. Finally, we describe a number of
optimisation techniques that are crucial in obtaining implementations of the
decision procedures, which, despite the worst-case complexity of the problem,
exhibit good performance with real-life problems.
|
cs/0005014
|
Practical Reasoning for Expressive Description Logics
|
cs.LO cs.AI
|
Description Logics (DLs) are a family of knowledge representation formalisms
mainly characterised by constructors to build complex concepts and roles from
atomic ones. Expressive role constructors are important in many applications,
but can be computationally problematical. We present an algorithm that decides
satisfiability of the DL ALC extended with transitive and inverse roles, role
hierarchies, and qualifying number restrictions. Early experiments indicate
that this algorithm is well-suited for implementation. Additionally, we show
that ALC extended with just transitive and inverse roles is still in PSPACE.
Finally, we investigate the limits of decidability for this family of DLs.
|
cs/0005015
|
Noun Phrase Recognition by System Combination
|
cs.CL
|
The performance of machine learning algorithms can be improved by combining
the output of different systems. In this paper we apply this idea to the
recognition of noun phrases.We generate different classifiers by using
different representations of the data. By combining the results with voting
techniques described in (Van Halteren et.al. 1998) we manage to improve the
best reported performances on standard data sets for base noun phrases and
arbitrary noun phrases.
|
cs/0005016
|
Improving Testsuites via Instrumentation
|
cs.CL
|
This paper explores the usefulness of a technique from software engineering,
namely code instrumentation, for the development of large-scale natural
language grammars. Information about the usage of grammar rules in test
sentences is used to detect untested rules, redundant test sentences, and
likely causes of overgeneration. Results show that less than half of a
large-coverage grammar for German is actually tested by two large testsuites,
and that 10-30% of testing time is redundant. The methodology applied can be
seen as a re-use of grammar writing knowledge for testsuite compilation.
|
cs/0005017
|
Reasoning with Individuals for the Description Logic SHIQ
|
cs.LO cs.AI
|
While there has been a great deal of work on the development of reasoning
algorithms for expressive description logics, in most cases only Tbox reasoning
is considered. In this paper we present an algorithm for combined Tbox and Abox
reasoning in the SHIQ description logic. This algorithm is of particular
interest as it can be used to decide the problem of (database) conjunctive
query containment w.r.t. a schema. Moreover, the realisation of an efficient
implementation should be relatively straightforward as it can be based on an
existing highly optimised implementation of the Tbox algorithm in the FaCT
system.
|
cs/0005019
|
On the Scalability of the Answer Extraction System "ExtrAns"
|
cs.CL
|
This paper reports on the scalability of the answer extraction system
ExtrAns. An answer extraction system locates the exact phrases in the documents
that contain the explicit answers to the user queries. Answer extraction
systems are therefore more convenient than document retrieval systems in
situations where the user wants to find specific information in limited time.
ExtrAns performs answer extraction over UNIX manpages. It has been
constructed by combining available linguistic resources and implementing only a
few modules from scratch. A resolution procedure between the minimal logical
form of the user query and the minimal logical forms of the manpage sentences
finds the answers to the queries. These answers are displayed to the user,
together with pointers to the respective manpages, and the exact phrases that
contribute to the answer are highlighted.
This paper shows that the increase in response times is not a big issue when
scaling the system up from 30 to 500 documents, and that the response times for
500 documents are still acceptable for a real-time answer extraction system.
|
cs/0005020
|
Centroid-based summarization of multiple documents: sentence extraction,
utility-based evaluation, and user studies
|
cs.CL cs.AI cs.DL cs.HC cs.IR
|
We present a multi-document summarizer, called MEAD, which generates
summaries using cluster centroids produced by a topic detection and tracking
system. We also describe two new techniques, based on sentence utility and
subsumption, which we have applied to the evaluation of both single and
multiple document summaries. Finally, we describe two user studies that test
our models of multi-document summarization.
|
cs/0005021
|
Modeling the Uncertainty in Complex Engineering Systems
|
cs.AI cs.LG
|
Existing procedures for model validation have been deemed inadequate for many
engineering systems. The reason of this inadequacy is due to the high degree of
complexity of the mechanisms that govern these systems. It is proposed in this
paper to shift the attention from modeling the engineering system itself to
modeling the uncertainty that underlies its behavior. A mathematical framework
for modeling the uncertainty in complex engineering systems is developed. This
framework uses the results of computational learning theory. It is based on the
premise that a system model is a learning machine.
|
cs/0005024
|
The SAT Phase Transition
|
cs.AI cs.CC
|
Phase transition is an important feature of SAT problem. For random k-SAT
model, it is proved that as r (ratio of clauses to variables) increases, the
structure of solutions will undergo a sudden change like satisfiability phase
transition when r reaches a threshold point. This phenomenon shows that the
satisfying truth assignments suddenly shift from being relatively different
from each other to being very similar to each other.
|
cs/0005025
|
Finite-State Reduplication in One-Level Prosodic Morphology
|
cs.CL
|
Reduplication, a central instance of prosodic morphology, is particularly
challenging for state-of-the-art computational morphology, since it involves
copying of some part of a phonological string. In this paper I advocate a
finite-state method that combines enriched lexical representations via
intersection to implement the copying. The proposal includes a
resource-conscious variant of automata and can benefit from the existence of
lazy algorithms. Finally, the implementation of a complex case from Koasati is
presented.
|
cs/0005026
|
A One-Time Pad based Cipher for Data Protection in Distributed
Environments
|
cs.CR cs.DC cs.IR cs.NI
|
A one-time pad (OTP) based cipher to insure both data protection and
integrity when mobile code arrives to a remote host is presented. Data
protection is required when a mobile agent could retrieve confidential
information that would be encrypted in untrusted nodes of the network; in this
case, information management could not rely on carrying an encryption key. Data
integrity is a prerequisite because mobile code must be protected against
malicious hosts that, by counterfeiting or removing collected data, could cover
information to the server that has sent the agent. The algorithm described in
this article seems to be simple enough, so as to be easily implemented. This
scheme is based on a non-interactive protocol and allows a remote host to
change its own data on-the-fly and, at the same time, protecting information
against handling by other hosts.
|
cs/0005027
|
A Bayesian Reflection on Surfaces
|
cs.CV cs.DS cs.LG math.PR nlin.AO physics.data-an
|
The topic of this paper is a novel Bayesian continuous-basis field
representation and inference framework. Within this paper several problems are
solved: The maximally informative inference of continuous-basis fields, that is
where the basis for the field is itself a continuous object and not
representable in a finite manner; the tradeoff between accuracy of
representation in terms of information learned, and memory or storage capacity
in bits; the approximation of probability distributions so that a maximal
amount of information about the object being inferred is preserved; an
information theoretic justification for multigrid methodology. The maximally
informative field inference framework is described in full generality and
denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the
update of field knowledge from previous knowledge at any scale, and new data,
to new knowledge at any other scale. An application example instance, the
inference of continuous surfaces from measurements (for example, camera image
data), is presented.
|
cs/0005028
|
A method for command identification, using modified collision free
hashing with addition & rotation iterative hash functions (part 1)
|
cs.HC cs.IR
|
This paper proposes a method for identification of a user`s fixed string set
(which can be a command/instruction set for a terminal or microprocessor). This
method is fast and has very small memory requirements, compared to a
traditional full string storage and compare method. The user feeds characters
into a microcontroller via a keyboard or another microprocessor sends commands
and the microcontroller hashes the input in order to identify valid commands,
ensuring no collisions between hashed valid strings, while applying further
criteria to narrow collision between random and valid strings. The method
proposed narrows the possibility of the latter kind of collision, achieving
small code and memory-size utilization and very fast execution. Hashing is
achieved using additive & rotating hash functions in an iterative form, which
can be very easily implemented in simple microcontrollers and microprocessors.
Such hash functions are presented and compared according to their efficiency
for a given string/command set, using the program found in the appendix.
|
cs/0005029
|
Ranking suspected answers to natural language questions using predictive
annotation
|
cs.CL
|
In this paper, we describe a system to rank suspected answers to natural
language questions. We process both corpus and query using a new technique,
predictive annotation, which augments phrases in texts with labels anticipating
their being targets of certain kinds of questions. Given a natural language
question, an IR system returns a set of matching passages, which are then
analyzed and ranked according to various criteria described in this paper. We
provide an evaluation of the techniques based on results from the TREC Q&A
evaluation in which our system participated.
|
cs/0005030
|
Axiomatizing Causal Reasoning
|
cs.AI cs.LO
|
Causal models defined in terms of a collection of equations, as defined by
Pearl, are axiomatized here. Axiomatizations are provided for three
successively more general classes of causal models: (1) the class of recursive
theories (those without feedback), (2) the class of theories where the
solutions to the equations are unique, (3) arbitrary theories (where the
equations may not have solutions and, if they do, they are not necessarily
unique). It is shown that to reason about causality in the most general third
class, we must extend the language used by Galles and Pearl. In addition, the
complexity of the decision procedures is characterized for all the languages
and classes of models considered.
|
cs/0005031
|
Conditional Plausibility Measures and Bayesian Networks
|
cs.AI
|
A general notion of algebraic conditional plausibility measures is defined.
Probability measures, ranking functions, possibility measures, and (under the
appropriate definitions) sets of probability measures can all be viewed as
defining algebraic conditional plausibility measures. It is shown that
algebraic conditional plausibility measures can be represented using Bayesian
networks.
|
cs/0006001
|
Boosting the Differences: A fast Bayesian classifier neural network
|
cs.CV
|
A Bayesian classifier that up-weights the differences in the attribute values
is discussed. Using four popular datasets from the UCI repository, some
interesting features of the network are illustrated. The network is suitable
for classification problems.
|
cs/0006002
|
Distorted English Alphabet Identification : An application of Difference
Boosting Algorithm
|
cs.CV
|
The difference-boosting algorithm is used on letters dataset from the UCI
repository to classify distorted raster images of English alphabets. In
contrast to rather complex networks, the difference-boosting is found to
produce comparable or better classification efficiency on this complex problem.
|
cs/0006003
|
Exploiting Diversity in Natural Language Processing: Combining Parsers
|
cs.CL
|
Three state-of-the-art statistical parsers are combined to produce more
accurate parses, as well as new bounds on achievable Treebank parsing accuracy.
Two general approaches are presented and two combination techniques are
described for each approach. Both parametric and non-parametric models are
explored. The resulting parsers surpass the best previously published
performance results for the Penn Treebank.
|
cs/0006005
|
Novelty Detection for Robot Neotaxis
|
cs.RO cs.NE nlin.AO
|
The ability of a robot to detect and respond to changes in its environment is
potentially very useful, as it draws attention to new and potentially important
features. We describe an algorithm for learning to filter out previously
experienced stimuli to allow further concentration on novel features. The
algorithm uses a model of habituation, a biological process which causes a
decrement in response with repeated presentation. Experiments with a mobile
robot are presented in which the robot detects the most novel stimulus and
turns towards it (`neotaxis').
|
cs/0006006
|
A Real-Time Novelty Detector for a Mobile Robot
|
cs.RO cs.NE
|
Recognising new or unusual features of an environment is an ability which is
potentially very useful to a robot. This paper demonstrates an algorithm which
achieves this task by learning an internal representation of `normality' from
sonar scans taken as a robot explores the environment. This model of the
environment is used to evaluate the novelty of each sonar scan presented to it
with relation to the model. Stimuli which have not been seen before, and
therefore have more novelty, are highlighted by the filter. The filter has the
ability to forget about features which have been learned, so that stimuli which
are seen only rarely recover their response over time. A number of robot
experiments are presented which demonstrate the operation of the filter.
|
cs/0006007
|
Novelty Detection on a Mobile Robot Using Habituation
|
cs.RO cs.NE nlin.AO
|
In this paper a novelty filter is introduced which allows a robot operating
in an un structured environment to produce a self-organised model of its
surroundings and to detect deviations from the learned model. The environment
is perceived using the rob ot's 16 sonar sensors. The algorithm produces a
novelty measure for each sensor scan relative to the model it has learned. This
means that it highlights stimuli which h ave not been previously experienced.
The novelty filter proposed uses a model of hab ituation. Habituation is a
decrement in behavioural response when a stimulus is pre sented repeatedly.
Robot experiments are presented which demonstrate the reliable o peration of
the filter in a number of environments.
|
cs/0006009
|
Knowledge and common knowledge in a distributed environment
|
cs.DC cs.AI
|
Reasoning about knowledge seems to play a fundamental role in distributed
systems. Indeed, such reasoning is a central part of the informal intuitive
arguments used in the design of distributed protocols. Communication in a
distributed system can be viewed as the act of transforming the system's state
of knowledge. This paper presents a general framework for formalizing and
reasoning about knowledge in distributed systems. We argue that states of
knowledge of groups of processors are useful concepts for the design and
analysis of distributed protocols. In particular, distributed knowledge
corresponds to knowledge that is ``distributed'' among the members of the
group, while common knowledge corresponds to a fact being ``publicly known''.
The relationship between common knowledge and a variety of desirable actions in
a distributed system is illustrated. Furthermore, it is shown that, formally
speaking, in practical systems common knowledge cannot be attained. A number of
weaker variants of common knowledge that are attainable in many cases of
interest are introduced and investigated.
|
cs/0006011
|
Bagging and Boosting a Treebank Parser
|
cs.CL
|
Bagging and boosting, two effective machine learning techniques, are applied
to natural language parsing. Experiments using these techniques with a
trainable statistical parser are described. The best resulting system provides
roughly as large of a gain in F-measure as doubling the corpus size. Error
analysis of the result of the boosting technique reveals some inconsistent
annotations in the Penn Treebank, suggesting a semi-automatic method for
finding inconsistent treebank annotations.
|
cs/0006012
|
Exploiting Diversity for Natural Language Parsing
|
cs.CL
|
The popularity of applying machine learning methods to computational
linguistics problems has produced a large supply of trainable natural language
processing systems. Most problems of interest have an array of off-the-shelf
products or downloadable code implementing solutions using various techniques.
Where these solutions are developed independently, it is observed that their
errors tend to be independently distributed. This thesis is concerned with
approaches for capitalizing on this situation in a sample problem domain, Penn
Treebank-style parsing.
The machine learning community provides techniques for combining outputs of
classifiers, but parser output is more structured and interdependent than
classifications. To address this discrepancy, two novel strategies for
combining parsers are used: learning to control a switch between parsers and
constructing a hybrid parse from multiple parsers' outputs.
Off-the-shelf parsers are not developed with an intention to perform well in
a collaborative ensemble. Two techniques are presented for producing an
ensemble of parsers that collaborate. All of the ensemble members are created
using the same underlying parser induction algorithm, and the method for
producing complementary parsers is only loosely constrained by that chosen
algorithm.
|
cs/0006013
|
An evaluation of Naive Bayesian anti-spam filtering
|
cs.CL cs.AI
|
It has recently been argued that a Naive Bayesian classifier can be used to
filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of
this proposal on a corpus that we make publicly available, contributing towards
standard benchmarks. At the same time we investigate the effect of
attribute-set size, training-corpus size, lemmatization, and stop-lists on the
filter's performance, issues that had not been previously explored. After
introducing appropriate cost-sensitive evaluation measures, we reach the
conclusion that additional safety nets are needed for the Naive Bayesian
anti-spam filter to be viable in practice.
|
cs/0006017
|
Turning Speech Into Scripts
|
cs.CL
|
We describe an architecture for implementing spoken natural language dialogue
interfaces to semi-autonomous systems, in which the central idea is to
transform the input speech signal through successive levels of representation
corresponding roughly to linguistic knowledge, dialogue knowledge, and domain
knowledge. The final representation is an executable program in a simple
scripting language equivalent to a subset of Cshell. At each stage of the
translation process, an input is transformed into an output, producing as a
byproduct a "meta-output" which describes the nature of the transformation
performed. We show how consistent use of the output/meta-output distinction
permits a simple and perspicuous treatment of apparently diverse topics
including resolution of pronouns, correction of user misconceptions, and
optimization of scripts. The methods described have been concretely realized in
a prototype speech interface to a simulation of the Personal Satellite
Assistant.
|
cs/0006018
|
Accuracy, Coverage, and Speed: What Do They Mean to Users?
|
cs.CL cs.HC
|
Speech is becoming increasingly popular as an interface modality, especially
in hands- and eyes-busy situations where the use of a keyboard or mouse is
difficult. However, despite the fact that many have hailed speech as being
inherently usable (since everyone already knows how to talk), most users of
speech input are left feeling disappointed by the quality of the interaction.
Clearly, there is much work to be done on the design of usable spoken
interfaces. We believe that there are two major problems in the design of
speech interfaces, namely, (a) the people who are currently working on the
design of speech interfaces are, for the most part, not interface designers and
therefore do not have as much experience with usability issues as we in the CHI
community do, and (b) speech, as an interface modality, has vastly different
properties than other modalities, and therefore requires different usability
measures.
|
cs/0006019
|
A Compact Architecture for Dialogue Management Based on Scripts and
Meta-Outputs
|
cs.CL
|
We describe an architecture for spoken dialogue interfaces to semi-autonomous
systems that transforms speech signals through successive representations of
linguistic, dialogue, and domain knowledge. Each step produces an output, and a
meta-output describing the transformation, with an executable program in a
simple scripting language as the final result. The output/meta-output
distinction permits perspicuous treatment of diverse tasks such as resolving
pronouns, correcting user misconceptions, and optimizing scripts.
|
cs/0006020
|
A Comparison of the XTAG and CLE Grammars for English
|
cs.CL
|
When people develop something intended as a large broad-coverage grammar,
they usually have a more specific goal in mind. Sometimes this goal is covering
a corpus; sometimes the developers have theoretical ideas they wish to
investigate; most often, work is driven by a combination of these two main
types of goal. What tends to happen after a while is that the community of
people working with the grammar starts thinking of some phenomena as
``central'', and makes serious efforts to deal with them; other phenomena are
labelled ``marginal'', and ignored. Before long, the distinction between
``central'' and ``marginal'' becomes so ingrained that it is automatic, and
people virtually stop thinking about the ``marginal'' phenomena. In practice,
the only way to bring the marginal things back into focus is to look at what
other people are doing and compare it with one's own work. In this paper, we
will take two large grammars, XTAG and the CLE, and examine each of them from
the other's point of view. We will find in both cases not only that important
things are missing, but that the perspective offered by the other grammar
suggests simple and practical ways of filling in the holes. It turns out that
there is a pleasing symmetry to the picture. XTAG has a very good treatment of
complement structure, which the CLE to some extent lacks; conversely, the CLE
offers a powerful and general account of adjuncts, which the XTAG grammar does
not fully duplicate. If we examine the way in which each grammar does the thing
it is good at, we find that the relevant methods are quite easy to port to the
other framework, and in fact only involve generalization and systematization of
existing mechanisms.
|
cs/0006021
|
Compiling Language Models from a Linguistically Motivated Unification
Grammar
|
cs.CL
|
Systems now exist which are able to compile unification grammars into
language models that can be included in a speech recognizer, but it is so far
unclear whether non-trivial linguistically principled grammars can be used for
this purpose. We describe a series of experiments which investigate the
question empirically, by incrementally constructing a grammar and discovering
what problems emerge when successively larger versions are compiled into finite
state graph representations and used as language models for a medium-vocabulary
recognition task.
|
cs/0006023
|
Dialogue Act Modeling for Automatic Tagging and Recognition of
Conversational Speech
|
cs.CL
|
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.
|
cs/0006024
|
Can Prosody Aid the Automatic Classification of Dialog Acts in
Conversational Speech?
|
cs.CL
|
Identifying whether an utterance is a statement, question, greeting, and so
forth is integral to effective automatic understanding of natural dialog.
Little is known, however, about how such dialog acts (DAs) can be automatically
classified in truly natural conversation. This study asks whether current
approaches, which use mainly word information, could be improved by adding
prosodic information. The study is based on more than 1000 conversations from
the Switchboard corpus. DAs were hand-annotated, and prosodic features
(duration, pause, F0, energy, and speaking rate) were automatically extracted
for each DA. In training, decision trees based on these features were inferred;
trees were then applied to unseen test data to evaluate performance.
Performance was evaluated for prosody models alone, and after combining the
prosody models with word information -- either from true words or from the
output of an automatic speech recognizer. For an overall classification task,
as well as three subtasks, prosody made significant contributions to
classification. Feature-specific analyses further revealed that although
canonical features (such as F0 for questions) were important, less obvious
features could compensate if canonical features were removed. Finally, in each
task, integrating the prosodic model with a DA-specific statistical language
model improved performance over that of the language model alone, especially
for the case of recognized words. Results suggest that DAs are redundantly
marked in natural conversation, and that a variety of automatically extractable
prosodic features could aid dialog processing in speech applications.
|
cs/0006025
|
Entropy-based Pruning of Backoff Language Models
|
cs.CL
|
A criterion for pruning parameters from N-gram backoff language models is
developed, based on the relative entropy between the original and the pruned
model. It is shown that the relative entropy resulting from pruning a single
N-gram can be computed exactly and efficiently for backoff models. The relative
entropy measure can be expressed as a relative change in training set
perplexity. This leads to a simple pruning criterion whereby all N-grams that
change perplexity by less than a threshold are removed from the model.
Experiments show that a production-quality Hub4 LM can be reduced to 26% its
original size without increasing recognition error. We also compare the
approach to a heuristic pruning criterion by Seymore and Rosenfeld (1996), and
show that their approach can be interpreted as an approximation to the relative
entropy criterion. Experimentally, both approaches select similar sets of
N-grams (about 85% overlap), with the exact relative entropy criterion giving
marginally better performance.
|
cs/0006027
|
Verbal Interactions in Virtual Worlds
|
cs.CL cs.HC
|
We first discuss respective advantages of language interaction in virtual
worlds and of using 3D images in dialogue systems. Then, we describe an example
of a verbal interaction system in virtual reality: Ulysse. Ulysse is a
conversational agent that helps a user navigate in virtual worlds. It has been
designed to be embedded in the representation of a participant of a virtual
conference and it responds positively to motion orders. Ulysse navigates the
user's viewpoint on his/her behalf in the virtual world. On tests we carried
out, we discovered that users, novices as well as experienced ones have
difficulties moving in a 3D environment. Agents such as Ulysse enable a user to
carry out navigation motions that would have been impossible with classical
interaction devices. From the whole Ulysse system, we have stripped off a
skeleton architecture that we have ported to VRML, Java, and Prolog. We hope
this skeleton helps the design of language applications in virtual worlds.
|
cs/0006028
|
Trainable Methods for Surface Natural Language Generation
|
cs.CL
|
We present three systems for surface natural language generation that are
trainable from annotated corpora. The first two systems, called NLG1 and NLG2,
require a corpus marked only with domain-specific semantic attributes, while
the last system, called NLG3, requires a corpus marked with both semantic
attributes and syntactic dependency information. All systems attempt to produce
a grammatical natural language phrase from a domain-specific semantic
representation. NLG1 serves a baseline system and uses phrase frequencies to
generate a whole phrase in one step, while NLG2 and NLG3 use maximum entropy
probability models to individually generate each word in the phrase. The
systems NLG2 and NLG3 learn to determine both the word choice and the word
order of the phrase. We present experiments in which we generate phrases to
describe flights in the air travel domain.
|
cs/0006030
|
Multiagent Control of Self-reconfigurable Robots
|
cs.RO cs.DC cs.MA
|
We demonstrate how multiagent systems provide useful control techniques for
modular self-reconfigurable (metamorphic) robots. Such robots consist of many
modules that can move relative to each other, thereby changing the overall
shape of the robot to suit different tasks. Multiagent control is particularly
well-suited for tasks involving uncertain and changing environments. We
illustrate this approach through simulation experiments of Proteo, a
metamorphic robot system currently under development.
|
cs/0006031
|
Verifying Termination of General Logic Programs with Concrete Queries
|
cs.AI cs.LO
|
We introduce a method of verifying termination of logic programs with respect
to concrete queries (instead of abstract query patterns). A necessary and
sufficient condition is established and an algorithm for automatic verification
is developed. In contrast to existing query pattern-based approaches, our
method has the following features: (1) It applies to all general logic programs
with non-floundering queries. (2) It is very easy to automate because it does
not need to search for a level mapping or a model, nor does it need to compute
an interargument relation based on additional mode or type information. (3) It
bridges termination analysis with loop checking, the two problems that have
been studied separately in the past despite their close technical relation with
each other.
|
cs/0006032
|
Estimation of English and non-English Language Use on the WWW
|
cs.CL cs.HC
|
The World Wide Web has grown so big, in such an anarchic fashion, that it is
difficult to describe. One of the evident intrinsic characteristics of the
World Wide Web is its multilinguality. Here, we present a technique for
estimating the size of a language-specific corpus given the frequency of
commonly occurring words in the corpus. We apply this technique to estimating
the number of words available through Web browsers for given languages.
Comparing data from 1996 to data from 1999 and 2000, we calculate the growth of
a number of European languages on the Web. As expected, non-English languages
are growing at a faster pace than English, though the position of English is
still dominant.
|
cs/0006036
|
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
|
cs.CL
|
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.
|
cs/0006038
|
Approximation and Exactness in Finite State Optimality Theory
|
cs.CL
|
Previous work (Frank and Satta 1998; Karttunen, 1998) has shown that
Optimality Theory with gradient constraints generally is not finite state. A
new finite-state treatment of gradient constraints is presented which improves
upon the approximation of Karttunen (1998). The method turns out to be exact,
and very compact, for the syllabification analysis of Prince and Smolensky
(1993).
|
cs/0006039
|
Orthogonal Least Squares Algorithm for the Approximation of a Map and
its Derivatives with a RBF Network
|
cs.NE cs.SD
|
Radial Basis Function Networks (RBFNs) are used primarily to solve
curve-fitting problems and for non-linear system modeling. Several algorithms
are known for the approximation of a non-linear curve from a sparse data set by
means of RBFNs. However, there are no procedures that permit to define
constrains on the derivatives of the curve. In this paper, the Orthogonal Least
Squares algorithm for the identification of RBFNs is modified to provide the
approximation of a non-linear 1-in 1-out map along with its derivatives, given
a set of training data. The interest on the derivatives of non-linear functions
concerns many identification and control tasks where the study of system
stability and robustness is addressed. The effectiveness of the proposed
algorithm is demonstrated by a study on the stability of a single loop feedback
system.
|
cs/0006040
|
Correlation over Decomposed Signals: A Non-Linear Approach to Fast and
Effective Sequences Comparison
|
cs.CV cs.DS q-bio
|
A novel non-linear approach to fast and effective comparison of sequences is
presented, compared to the traditional cross-correlation operator, and
illustrated with respect to DNA sequences.
|
cs/0006041
|
Using a Diathesis Model for Semantic Parsing
|
cs.CL cs.AI
|
This paper presents a semantic parsing approach for unrestricted texts.
Semantic parsing is one of the major bottlenecks of Natural Language
Understanding (NLU) systems and usually requires building expensive resources
not easily portable to other domains. Our approach obtains a case-role
analysis, in which the semantic roles of the verb are identified. In order to
cover all the possible syntactic realisations of a verb, our system combines
their argument structure with a set of general semantic labelled diatheses
models. Combining them, the system builds a set of syntactic-semantic patterns
with their own role-case representation. Once the patterns are build, we use an
approximate tree pattern-matching algorithm to identify the most reliable
pattern for a sentence. The pattern matching is performed between the
syntactic-semantic patterns and the feature-structure tree representing the
morphological, syntactical and semantic information of the analysed sentence.
For sentences assigned to the correct model, the semantic parsing system we are
presenting identifies correctly more than 73% of possible semantic case-roles.
|
cs/0006042
|
Semantic Parsing based on Verbal Subcategorization
|
cs.CL cs.AI
|
The aim of this work is to explore new methodologies on Semantic Parsing for
unrestricted texts. Our approach follows the current trends in Information
Extraction (IE) and is based on the application of a verbal subcategorization
lexicon (LEXPIR) by means of complex pattern recognition techniques. LEXPIR is
framed on the theoretical model of the verbal subcategorization developed in
the Pirapides project.
|
cs/0006043
|
Constraint compiling into rules formalism constraint compiling into
rules formalism for dynamic CSPs computing
|
cs.AI
|
In this paper we present a rule based formalism for filtering variables
domains of constraints. This formalism is well adapted for solving dynamic CSP.
We take diagnosis as an instance problem to illustrate the use of these rules.
A diagnosis problem is seen like finding all the minimal sets of constraints to
be relaxed in the constraint network that models the device to be diagnosed
|
cs/0006044
|
Finite-State Non-Concatenative Morphotactics
|
cs.CL
|
Finite-state morphology in the general tradition of the Two-Level and Xerox
implementations has proved very successful in the production of robust
morphological analyzer-generators, including many large-scale commercial
systems. However, it has long been recognized that these implementations have
serious limitations in handling non-concatenative phenomena. We describe a new
technique for constructing finite-state transducers that involves reapplying
the regular-expression compiler to its own output. Implemented in an algorithm
called compile-replace, this technique has proved useful for handling
non-concatenative phenomena; and we demonstrate it on Malay full-stem
reduplication and Arabic stem interdigitation.
|
cs/0006047
|
Geometric Morphology of Granular Materials
|
cs.CV
|
We present a new method to transform the spectral pixel information of a
micrograph into an affine geometric description, which allows us to analyze the
morphology of granular materials. We use spectral and pulse-coupled neural
network based segmentation techniques to generate blobs, and a newly developed
algorithm to extract dilated contours. A constrained Delaunay tesselation of
the contour points results in a triangular mesh. This mesh is the basic
ingredient of the Chodal Axis Transform, which provides a morphological
decomposition of shapes. Such decomposition allows for grain separation and the
efficient computation of the statistical features of granular materials.
|
cs/0007001
|
Constraint Exploration and Envelope of Simulation Trajectories
|
cs.PL cs.AI cs.LO
|
The implicit theory that a simulation represents is precisely not in the
individual choices but rather in the 'envelope' of possible trajectories - what
is important is the shape of the whole envelope. Typically a huge amount of
computation is required when experimenting with factors bearing on the dynamics
of a simulation to tease out what affects the shape of this envelope. In this
paper we present a methodology aimed at systematically exploring this envelope.
We propose a method for searching for tendencies and proving their necessity
relative to a range of parameterisations of the model and agents' choices, and
to the logic of the simulation language. The exploration consists of a forward
chaining generation of the trajectories associated to and constrained by such a
range of parameterisations and choices. Additionally, we propose a
computational procedure that helps implement this exploration by translating a
Multi Agent System simulation into a constraint-based search over possible
trajectories by 'compiling' the simulation rules into a more specific form,
namely by partitioning the simulation rules using appropriate modularity in the
simulation. An example of this procedure is exhibited.
Keywords: Constraint Search, Constraint Logic Programming, Proof, Emergence,
Tendencies
|
cs/0007002
|
Interval Constraint Solving for Camera Control and Motion Planning
|
cs.AI cs.NA math.NA
|
Many problems in robust control and motion planning can be reduced to either
find a sound approximation of the solution space determined by a set of
nonlinear inequalities, or to the ``guaranteed tuning problem'' as defined by
Jaulin and Walter, which amounts to finding a value for some tuning parameter
such that a set of inequalities be verified for all the possible values of some
perturbation vector. A classical approach to solve these problems, which
satisfies the strong soundness requirement, involves some quantifier
elimination procedure such as Collins' Cylindrical Algebraic Decomposition
symbolic method. Sound numerical methods using interval arithmetic and local
consistency enforcement to prune the search space are presented in this paper
as much faster alternatives for both soundly solving systems of nonlinear
inequalities, and addressing the guaranteed tuning problem whenever the
perturbation vector has dimension one. The use of these methods in camera
control is investigated, and experiments with the prototype of a declarative
modeller to express camera motion using a cinematic language are reported and
commented.
|
cs/0007003
|
Using compression to identify acronyms in text
|
cs.DL cs.IR
|
Text mining is about looking for patterns in natural language text, and may
be defined as the process of analyzing text to extract information from it for
particular purposes. In previous work, we claimed that compression is a key
technology for text mining, and backed this up with a study that showed how
particular kinds of lexical tokens---names, dates, locations, etc.---can be
identified and located in running text, using compression models to provide the
leverage necessary to distinguish different token types (Witten et al., 1999)
|
cs/0007004
|
Brainstorm/J: a Java Framework for Intelligent Agents
|
cs.AI
|
Despite the effort of many researchers in the area of multi-agent systems
(MAS) for designing and programming agents, a few years ago the research
community began to take into account that common features among different MAS
exists. Based on these common features, several tools have tackled the problem
of agent development on specific application domains or specific types of
agents. As a consequence, their scope is restricted to a subset of the huge
application domain of MAS. In this paper we propose a generic infrastructure
for programming agents whose name is Brainstorm/J. The infrastructure has been
implemented as an object oriented framework. As a consequence, our approach
supports a broader scope of MAS applications than previous efforts, being
flexible and reusable.
|
cs/0007009
|
Incremental construction of minimal acyclic finite-state automata
|
cs.CL
|
In this paper, we describe a new method for constructing minimal,
deterministic, acyclic finite-state automata from a set of strings. Traditional
methods consist of two phases: the first to construct a trie, the second one to
minimize it. Our approach is to construct a minimal automaton in a single phase
by adding new strings one by one and minimizing the resulting automaton
on-the-fly. We present a general algorithm as well as a specialization that
relies upon the lexicographical ordering of the input strings.
|
cs/0007010
|
Boosting Applied to Word Sense Disambiguation
|
cs.CL cs.AI
|
In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied
to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of
15 selected polysemous words show that the boosting approach surpasses Naive
Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy
on supervised WSD. In order to make boosting practical for a real learning
domain of thousands of words, several ways of accelerating the algorithm by
reducing the feature space are studied. The best variant, which we call
LazyBoosting, is tested on the largest sense-tagged corpus available containing
192,800 examples of the 191 most frequent and ambiguous English words. Again,
boosting compares favourably to the other benchmark algorithms.
|
cs/0007011
|
Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation
Revisited
|
cs.CL cs.AI
|
This paper describes an experimental comparison between two standard
supervised learning methods, namely Naive Bayes and Exemplar-based
classification, on the Word Sense Disambiguation (WSD) problem. The aim of the
work is twofold. Firstly, it attempts to contribute to clarify some confusing
information about the comparison between both methods appearing in the related
literature. In doing so, several directions have been explored, including:
testing several modifications of the basic learning algorithms and varying the
feature space. Secondly, an improvement of both algorithms is proposed, in
order to deal with large attribute sets. This modification, which basically
consists in using only the positive information appearing in the examples,
allows to improve greatly the efficiency of the methods, with no loss in
accuracy. The experiments have been performed on the largest sense-tagged
corpus available containing the most frequent and ambiguous English words.
Results show that the Exemplar-based approach to WSD is generally superior to
the Bayesian approach, especially when a specific metric for dealing with
symbolic attributes is used.
|
cs/0007012
|
Using Learning-based Filters to Detect Rule-based Filtering Obsolescence
|
cs.CL cs.AI
|
For years, Caisse des Depots et Consignations has produced information
filtering applications. To be operational, these applications require high
filtering performances which are achieved by using rule-based filters. With
this technique, an administrator has to tune a set of rules for each topic.
However, filters become obsolescent over time. The decrease of their
performances is due to diachronic polysemy of terms that involves a loss of
precision and to diachronic polymorphism of concepts that involves a loss of
recall.
To help the administrator to maintain his filters, we have developed a method
which automatically detects filtering obsolescence. It consists in making a
learning-based control filter using a set of documents which have already been
categorised as relevant or not relevant by the rule-based filter. The idea is
to supervise this filter by processing a differential comparison of its
outcomes with those of the control one.
This method has many advantages. It is simple to implement since the training
set used by the learning is supplied by the rule-based filter. Thus, both the
making and the use of the control filter are fully automatic. With automatic
detection of obsolescence, learning-based filtering finds a rich application
which offers interesting prospects.
|
cs/0007013
|
Applying Constraint Handling Rules to HPSG
|
cs.CL cs.PL
|
Constraint Handling Rules (CHR) have provided a realistic solution to an
over-arching problem in many fields that deal with constraint logic
programming: how to combine recursive functions or relations with constraints
while avoiding non-termination problems. This paper focuses on some other
benefits that CHR, specifically their implementation in SICStus Prolog, have
provided to computational linguists working on grammar design tools. CHR rules
are applied by means of a subsumption check and this check is made only when
their variables are instantiated or bound. The former functionality is at best
difficult to simulate using more primitive coroutining statements such as
SICStus when/2, and the latter simply did not exist in any form before CHR.
For the sake of providing a case study in how these can be applied to grammar
development, we consider the Attribute Logic Engine (ALE), a Prolog
preprocessor for logic programming with typed feature structures, and its
extension to a complete grammar development system for Head-driven Phrase
Structure Grammar (HPSG), a popular constraint-based linguistic theory that
uses typed feature structures. In this context, CHR can be used not only to
extend the constraint language of feature structure descriptions to include
relations in a declarative way, but also to provide support for constraints
with complex antecedents and constraints on the co-occurrence of feature values
that are necessary to interpret the type system of HPSG properly.
|
cs/0007016
|
Two Steps Feature Selection and Neural Network Classification for the
TREC-8 Routing
|
cs.CL cs.AI
|
For the TREC-8 routing, one specific filter is built for each topic. Each
filter is a classifier trained to recognize the documents that are relevant to
the topic. When presented with a document, each classifier estimates the
probability for the document to be relevant to the topic for which it has been
trained. Since the procedure for building a filter is topic-independent, the
system is fully automatic.
By making use of a sample of documents that have previously been evaluated as
relevant or not relevant to a particular topic, a term selection is performed,
and a neural network is trained. Each document is represented by a vector of
frequencies of a list of selected terms. This list depends on the topic to be
filtered; it is constructed in two steps. The first step defines the
characteristic words used in the relevant documents of the corpus; the second
one chooses, among the previous list, the most discriminant ones. The length of
the vector is optimized automatically for each topic. At the end of the term
selection, a vector of typically 25 words is defined for the topic, so that
each document which has to be processed is represented by a vector of term
frequencies.
This vector is subsequently input to a classifier that is trained from the
same sample. After training, the classifier estimates for each document of a
test set its probability of being relevant; for submission to TREC, the top
1000 documents are ranked in order of decreasing relevance.
|
cs/0007017
|
Fuzzy data: XML may handle it
|
cs.IR
|
Data modeling is one of the most difficult tasks in application engineering.
The engineer must be aware of the use cases and the required application
services and at a certain point of time he has to fix the data model which
forms the base for the application services. However, once the data model has
been fixed it is difficult to consider changing needs. This might be a problem
in specific domains, which are as dynamic as the healthcare domain. With fuzzy
data we address all those data that are difficult to organize in a single
database. In this paper we discuss a gradual and pragmatic approach that uses
the XML technology to conquer more model flexibility. XML may provide the clue
between unstructured text data and structured database solutions and shift the
paradigm from "organizing the data along a given model" towards "organizing the
data along user requirements".
|
cs/0007018
|
Bootstrapping a Tagged Corpus through Combination of Existing
Heterogeneous Taggers
|
cs.CL
|
This paper describes a new method, Combi-bootstrap, to exploit existing
taggers and lexical resources for the annotation of corpora with new tagsets.
Combi-bootstrap uses existing resources as features for a second level machine
learning module, that is trained to make the mapping to the new tagset on a
very small sample of annotated corpus material. Experiments show that
Combi-bootstrap: i) can integrate a wide variety of existing resources, and ii)
achieves much higher accuracy (up to 44.7 % error reduction) than both the best
single tagger and an ensemble tagger constructed out of the same small training
sample.
|
cs/0007020
|
Polynomial-time Computation via Local Inference Relations
|
cs.LO cs.AI cs.PL
|
We consider the concept of a local set of inference rules. A local rule set
can be automatically transformed into a rule set for which bottom-up evaluation
terminates in polynomial time. The local-rule-set transformation gives
polynomial-time evaluation strategies for a large variety of rule sets that
cannot be given terminating evaluation strategies by any other known automatic
technique. This paper discusses three new results. First, it is shown that
every polynomial-time predicate can be defined by an (unstratified) local rule
set. Second, a new machine-recognizable subclass of the local rule sets is
identified. Finally we show that locality, as a property of rule sets, is
undecidable in general.
|
cs/0007022
|
ATLAS: A flexible and extensible architecture for linguistic annotation
|
cs.CL
|
We describe a formal model for annotating linguistic artifacts, from which we
derive an application programming interface (API) to a suite of tools for
manipulating these annotations. The abstract logical model provides for a range
of storage formats and promotes the reuse of tools that interact through this
API. We focus first on ``Annotation Graphs,'' a graph model for annotations on
linear signals (such as text and speech) indexed by intervals, for which
efficient database storage and querying techniques are applicable. We note how
a wide range of existing annotated corpora can be mapped to this annotation
graph model. This model is then generalized to encompass a wider variety of
linguistic ``signals,'' including both naturally occuring phenomena (as
recorded in images, video, multi-modal interactions, etc.), as well as the
derived resources that are increasingly important to the engineering of natural
language processing systems (such as word lists, dictionaries, aligned
bilingual corpora, etc.). We conclude with a review of the current efforts
towards implementing key pieces of this architecture.
|
cs/0007023
|
Towards a query language for annotation graphs
|
cs.CL cs.DB
|
The multidimensional, heterogeneous, and temporal nature of speech databases
raises interesting challenges for representation and query. Recently,
annotation graphs have been proposed as a general-purpose representational
framework for speech databases. Typical queries on annotation graphs require
path expressions similar to those used in semistructured query languages.
However, the underlying model is rather different from the customary graph
models for semistructured data: the graph is acyclic and unrooted, and both
temporal and inclusion relationships are important. We develop a query language
and describe optimization techniques for an underlying relational
representation.
|
cs/0007024
|
Many uses, many annotations for large speech corpora: Switchboard and
TDT as case studies
|
cs.CL
|
This paper discusses the challenges that arise when large speech corpora
receive an ever-broadening range of diverse and distinct annotations. Two case
studies of this process are presented: the Switchboard Corpus of telephone
conversations and the TDT2 corpus of broadcast news. Switchboard has undergone
two independent transcriptions and various types of additional annotation, all
carried out as separate projects that were dispersed both geographically and
chronologically. The TDT2 corpus has also received a variety of annotations,
but all directly created or managed by a core group. In both cases, issues
arise involving the propagation of repairs, consistency of references, and the
ability to integrate annotations having different formats and levels of detail.
We describe a general framework whereby these issues can be addressed
successfully.
|
cs/0007026
|
Integrating E-Commerce and Data Mining: Architecture and Challenges
|
cs.LG cs.AI cs.CV cs.DB
|
We show that the e-commerce domain can provide all the right ingredients for
successful data mining and claim that it is a killer domain for data mining. We
describe an integrated architecture, based on our expe-rience at Blue Martini
Software, for supporting this integration. The architecture can dramatically
reduce the pre-processing, cleaning, and data understanding effort often
documented to take 80% of the time in knowledge discovery projects. We
emphasize the need for data collection at the application server layer (not the
web server) in order to support logging of data and metadata that is essential
to the discovery process. We describe the data transformation bridges required
from the transaction processing systems and customer event streams (e.g.,
clickstreams) to the data warehouse. We detail the mining workbench, which
needs to provide multiple views of the data through reporting, data mining
algorithms, visualization, and OLAP. We con-clude with a set of challenges.
|
cs/0007031
|
Parameter-free Model of Rank Polysemantic Distribution
|
cs.CL
|
A model of rank polysemantic distribution with a minimal number of fitting
parameters is offered. In an ideal case a parameter-free description of the
dependence on the basis of one or several immediate features of the
distribution is possible.
|
cs/0007032
|
Knowledge on Treelike Spaces
|
cs.LO cs.AI
|
This paper presents a bimodal logic for reasoning about knowledge during
knowledge acquisition. One of the modalities represents (effort during)
non-deterministic time and the other represents knowledge. The semantics of
this logic are tree-like spaces which are a generalization of semantics used
for modeling branching time and historical necessity. A finite system of axiom
schemes is shown to be canonically complete for the formentioned spaces. A
characterization of the satisfaction relation implies the small model property
and decidability for this system.
|
cs/0007033
|
To Preference via Entrenchment
|
cs.LO cs.AI
|
We introduce a simple generalization of Gardenfors and Makinson's epistemic
entrenchment called partial entrenchment. We show that preferential inference
can be generated as the sceptical counterpart of an inference mechanism defined
directly on partial entrenchment.
|
cs/0007035
|
Mapping WordNets Using Structural Information
|
cs.CL
|
We present a robust approach for linking already existing lexical/semantic
hierarchies. We used a constraint satisfaction algorithm (relaxation labeling)
to select --among a set of candidates-- the node in a target taxonomy that
bests matches each node in a source taxonomy. In particular, we use it to map
the nominal part of WordNet 1.5 onto WordNet 1.6, with a very high precision
and a very low remaining ambiguity.
|
cs/0007036
|
Language identification of controlled systems: Modelling, control and
anomaly detection
|
cs.CL
|
Formal language techniques have been used in the past to study autonomous
dynamical systems. However, for controlled systems, new features are needed to
distinguish between information generated by the system and input control. We
show how the modelling framework for controlled dynamical systems leads
naturally to a formulation in terms of context-dependent grammars. A learning
algorithm is proposed for on-line generation of the grammar productions, this
formulation being then used for modelling, control and anomaly detection.
Practical applications are described for electromechanical drives. Grammatical
interpolation techniques yield accurate results and the pattern detection
capabilities of the language-based formulation makes it a promising technique
for the early detection of anomalies or faulty behaviour.
|
cs/0007038
|
Modal Logics for Topological Spaces
|
cs.LO cs.AI
|
In this thesis we shall present two logical systems, MP and MP, for the
purpose of reasoning about knowledge and effort. These logical systems will be
interpreted in a spatial context and therefore, the abstract concepts of
knowledge and effort will be defined by concrete mathematical concepts.
|
cs/0007039
|
Ordering-based Representations of Rational Inference
|
cs.LO cs.AI
|
Rational inference relations were introduced by Lehmann and Magidor as the
ideal systems for drawing conclusions from a conditional base. However, there
has been no simple characterization of these relations, other than its original
representation by preferential models. In this paper, we shall characterize
them with a class of total preorders of formulas by improving and extending
Gardenfors and Makinson's results for expectation inference relations. A second
representation is application-oriented and is obtained by considering a class
of consequence operators that grade sets of defaults according to our reliance
on them. The finitary fragment of this class of consequence operators has been
employed by recent default logic formalisms based on maxiconsistency.
|
cs/0007040
|
Entrenchment Relations: A Uniform Approach to Nonmonotonicity
|
cs.LO cs.AI
|
We show that Gabbay's nonmonotonic consequence relations can be reduced to a
new family of relations, called entrenchment relations. Entrenchment relations
provide a direct generalization of epistemic entrenchment and expectation
ordering introduced by Gardenfors and Makinson for the study of belief revision
and expectation inference, respectively.
|
cs/0007041
|
Relevance as Deduction: A Logical View of Information Retrieval
|
cs.IR cs.LO
|
The problem of Information Retrieval is, given a set of documents D and a
query q, providing an algorithm for retrieving all documents in D relevant to
q. However, retrieval should depend and be updated whenever the user is able to
provide as an input a preferred set of relevant documents; this process is
known as em relevance feedback. Recent work in IR has been paying great
attention to models which employ a logical approach; the advantage being that
one can have a simple computable characterization of retrieval on the basis of
a pure logical analysis of retrieval. Most of the logical models make use of
probabilities or similar belief functions in order to introduce the inductive
component whereby uncertainty is treated. Their general paradigm is the
following: em find the nature of conditional $d\imp q$ and then define a
probability on the top of it. We just reverse this point of view; first use the
numerical information, frequencies or probabilities, then define your own
logical consequence. More generally, we claim that retrieval is a form of
deduction. We introduce a simple but powerful logical framework of relevance
feedback, derived from the well founded area of nonmonotonic logic. This
description can help us evaluate, describe and compare from a theoretical point
of view previous approaches based on conditionals or probabilities.
|
cs/0007044
|
Managing Periodically Updated Data in Relational Databases: A Stochastic
Modeling Approach
|
cs.DB
|
Recent trends in information management involve the periodic transcription of
data onto secondary devices in a networked environment, and the proper
scheduling of these transcriptions is critical for efficient data management.
To assist in the scheduling process, we are interested in modeling the
reduction of consistency over time between a relation and its replica, termed
obsolescence of data. The modeling is based on techniques from the field of
stochastic processes, and provides several stochastic models for content
evolution in the base relations of a database, taking referential integrity
constraints into account. These models are general enough to accommodate most
of the common scenarios in databases, including batch insertions and life spans
both with and without memory. As an initial "proof of concept" of the
applicability of our approach, we validate the insertion portion of our model
framework via experiments with real data feeds. We also discuss a set of
transcription protocols which make use of the proposed stochastic model.
|
cs/0008003
|
Interfacing Constraint-Based Grammars and Generation Algorithms
|
cs.CL
|
Constraint-based grammars can, in principle, serve as the major linguistic
knowledge source for both parsing and generation. Surface generation starts
from input semantics representations that may vary across grammars. For many
declarative grammars, the concept of derivation implicitly built in is that of
parsing. They may thus not be interpretable by a generation algorithm. We show
that linguistically plausible semantic analyses can cause severe problems for
semantic-head-driven approaches for generation (SHDG). We use SeReal, a variant
of SHDG and the DISCO grammar of German as our source of examples. We propose a
new, general approach that explicitly accounts for the interface between the
grammar and the generation algorithm by adding a control-oriented layer to the
linguistic knowledge base that reorganizes the semantics in a way suitable for
generation.
|
cs/0008004
|
Comparing two trainable grammatical relations finders
|
cs.CL
|
Grammatical relationships (GRs) form an important level of natural language
processing, but different sets of GRs are useful for different purposes.
Therefore, one may often only have time to obtain a small training corpus with
the desired GR annotations. On such a small training corpus, we compare two
systems. They use different learning techniques, but we find that this
difference by itself only has a minor effect. A larger factor is that in
English, a different GR length measure appears better suited for finding simple
argument GRs than for finding modifier GRs. We also find that partitioning the
data may help memory-based learning.
|
cs/0008005
|
More accurate tests for the statistical significance of result
differences
|
cs.CL
|
Statistical significance testing of differences in values of metrics like
recall, precision and balanced F-score is a necessary part of empirical natural
language processing. Unfortunately, we find in a set of experiments that many
commonly used tests often underestimate the significance and so are less likely
to detect differences that exist between different techniques. This
underestimation comes from an independence assumption that is often violated.
We point out some useful tests that do not make this assumption, including
computationally-intensive randomization tests.
|
cs/0008007
|
Tagger Evaluation Given Hierarchical Tag Sets
|
cs.CL
|
We present methods for evaluating human and automatic taggers that extend
current practice in three ways. First, we show how to evaluate taggers that
assign multiple tags to each test instance, even if they do not assign
probabilities. Second, we show how to accommodate a common property of manually
constructed ``gold standards'' that are typically used for objective
evaluation, namely that there is often more than one correct answer. Third, we
show how to measure performance when the set of possible tags is
tree-structured in an IS-A hierarchy. To illustrate how our methods can be used
to measure inter-annotator agreement, we show how to compute the kappa
coefficient over hierarchical tag sets.
|
cs/0008008
|
On the Average Similarity Degree between Solutions of Random k-SAT and
Random CSPs
|
cs.AI cs.CC cs.DM
|
To study the structure of solutions for random k-SAT and random CSPs, this
paper introduces the concept of average similarity degree to characterize how
solutions are similar to each other. It is proved that under certain
conditions, as r (i.e. the ratio of constraints to variables) increases, the
limit of average similarity degree when the number of variables approaches
infinity exhibits phase transitions at a threshold point, shifting from a
smaller value to a larger value abruptly. For random k-SAT this phenomenon will
occur when k>4 . It is further shown that this threshold point is also a
singular point with respect to r in the asymptotic estimate of the second
moment of the number of solutions. Finally, we discuss how this work is helpful
to understand the hardness of solving random instances and a possible
application of it to the design of search algorithms.
|
cs/0008009
|
Data Mining to Measure and Improve the Success of Web Sites
|
cs.LG cs.DB
|
For many companies, competitiveness in e-commerce requires a successful
presence on the web. Web sites are used to establish the company's image, to
promote and sell goods and to provide customer support. The success of a web
site affects and reflects directly the success of the company in the electronic
market. In this study, we propose a methodology to improve the ``success'' of
web sites, based on the exploitation of navigation pattern discovery. In
particular, we present a theory, in which success is modelled on the basis of
the navigation behaviour of the site's users. We then exploit WUM, a navigation
pattern discovery miner, to study how the success of a site is reflected in the
users' behaviour. With WUM we measure the success of a site's components and
obtain concrete indications of how the site should be improved. We report on
our first experiments with an online catalog, the success of which we have
studied. Our mining analysis has shown very promising results, on the basis of
which the site is currently undergoing concrete improvements.
|
cs/0008012
|
Applying System Combination to Base Noun Phrase Identification
|
cs.CL
|
We use seven machine learning algorithms for one task: identifying base noun
phrases. The results have been processed by different system combination
methods and all of these outperformed the best individual result. We have
applied the seven learners with the best combinator, a majority vote of the top
five systems, to a standard data set and managed to improve the best published
result for this data set.
|
cs/0008013
|
Meta-Learning for Phonemic Annotation of Corpora
|
cs.CL
|
We apply rule induction, classifier combination and meta-learning (stacked
classifiers) to the problem of bootstrapping high accuracy automatic annotation
of corpora with pronunciation information. The task we address in this paper
consists of generating phonemic representations reflecting the Flemish and
Dutch pronunciations of a word on the basis of its orthographic representation
(which in turn is based on the actual speech recordings). We compare several
possible approaches to achieve the text-to-pronunciation mapping task:
memory-based learning, transformation-based learning, rule induction, maximum
entropy modeling, combination of classifiers in stacked learning, and stacking
of meta-learners. We are interested both in optimal accuracy and in obtaining
insight into the linguistic regularities involved. As far as accuracy is
concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at
word level) for single classifiers is boosted significantly with additional
error reductions of 31% and 38% respectively using combination of classifiers,
and a further 5% using combination of meta-learners, bringing overall word
level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We
also show that the application of machine learning methods indeed leads to
increased insight into the linguistic regularities determining the variation
between the two pronunciation variants studied.
|
cs/0008014
|
Aspects of Pattern-Matching in Data-Oriented Parsing
|
cs.CL
|
Data-Oriented Parsing (dop) ranks among the best parsing schemes, pairing
state-of-the art parsing accuracy to the psycholinguistic insight that larger
chunks of syntactic structures are relevant grammatical and probabilistic
units. Parsing with the dop-model, however, seems to involve a lot of CPU
cycles and a considerable amount of double work, brought on by the concept of
multiple derivations, which is necessary for probabilistic processing, but
which is not convincingly related to a proper linguistic backbone. It is
however possible to re-interpret the dop-model as a pattern-matching model,
which tries to maximize the size of the substructures that construct the parse,
rather than the probability of the parse. By emphasizing this memory-based
aspect of the dop-model, it is possible to do away with multiple derivations,
opening up possibilities for efficient Viterbi-style optimizations, while still
retaining acceptable parsing accuracy through enhanced context-sensitivity.
|
cs/0008015
|
Temiar Reduplication in One-Level Prosodic Morphology
|
cs.CL
|
Temiar reduplication is a difficult piece of prosodic morphology. This paper
presents the first computational analysis of Temiar reduplication, using the
novel finite-state approach of One-Level Prosodic Morphology originally
developed by Walther (1999b, 2000). After reviewing both the data and the basic
tenets of One-level Prosodic Morphology, the analysis is laid out in some
detail, using the notation of the FSA Utilities finite-state toolkit (van Noord
1997). One important discovery is that in this approach one can easily define a
regular expression operator which ambiguously scans a string in the left- or
rightward direction for a certain prosodic property. This yields an elegant
account of base-length-dependent triggering of reduplication as found in
Temiar.
|
cs/0008016
|
Processing Self Corrections in a speech to speech system
|
cs.CL cs.AI
|
Speech repairs occur often in spontaneous spoken dialogues. The ability to
detect and correct those repairs is necessary for any spoken language system.
We present a framework to detect and correct speech repairs where all relevant
levels of information, i.e., acoustics, lexis, syntax and semantics can be
integrated. The basic idea is to reduce the search space for repairs as soon as
possible by cascading filters that involve more and more features. At first an
acoustic module generates hypotheses about the existence of a repair. Second a
stochastic model suggests a correction for every hypothesis. Well scored
corrections are inserted as new paths in the word lattice. Finally a lattice
parser decides on accepting the rep air.
|
cs/0008017
|
Efficient probabilistic top-down and left-corner parsing
|
cs.CL
|
This paper examines efficient predictive broad-coverage parsing without
dynamic programming. In contrast to bottom-up methods, depth-first top-down
parsing produces partial parses that are fully connected trees spanning the
entire left context, from which any kind of non-local dependency or partial
semantic interpretation can in principle be read. We contrast two predictive
parsing approaches, top-down and left-corner parsing, and find both to be
viable. In addition, we find that enhancement with non-local information not
only improves parser accuracy, but also substantially improves the search
efficiency.
|
cs/0008019
|
An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam
Filtering with Personal E-mail Messages
|
cs.CL cs.IR cs.LG
|
The growing problem of unsolicited bulk e-mail, also known as "spam", has
generated a need for reliable anti-spam e-mail filters. Filters of this type
have so far been based mostly on manually constructed keyword patterns. An
alternative approach has recently been proposed, whereby a Naive Bayesian
classifier is trained automatically to detect spam messages. We test this
approach on a large collection of personal e-mail messages, which we make
publicly available in "encrypted" form contributing towards standard
benchmarks. We introduce appropriate cost-sensitive measures, investigating at
the same time the effect of attribute-set size, training-corpus size,
lemmatization, and stop lists, issues that have not been explored in previous
experiments. Finally, the Naive Bayesian filter is compared, in terms of
performance, to a filter that uses keyword patterns, and which is part of a
widely used e-mail reader.
|
cs/0008020
|
Explaining away ambiguity: Learning verb selectional preference with
Bayesian networks
|
cs.CL cs.AI
|
This paper presents a Bayesian model for unsupervised learning of verb
selectional preferences. For each verb the model creates a Bayesian network
whose architecture is determined by the lexical hierarchy of Wordnet and whose
parameters are estimated from a list of verb-object pairs found from a corpus.
``Explaining away'', a well-known property of Bayesian networks, helps the
model deal in a natural fashion with word sense ambiguity in the training data.
On a word sense disambiguation test our model performed better than other state
of the art systems for unsupervised learning of selectional preferences.
Computational complexity problems, ways of improving this approach and methods
for implementing ``explaining away'' in other graphical frameworks are
discussed.
|
cs/0008021
|
Compact non-left-recursive grammars using the selective left-corner
transform and factoring
|
cs.CL
|
The left-corner transform removes left-recursion from (probabilistic)
context-free grammars and unification grammars, permitting simple top-down
parsing techniques to be used. Unfortunately the grammars produced by the
standard left-corner transform are usually much larger than the original. The
selective left-corner transform described in this paper produces a transformed
grammar which simulates left-corner recognition of a user-specified set of the
original productions, and top-down recognition of the others. Combined with two
factorizations, it produces non-left-recursive grammars that are not much
larger than the original.
|
cs/0008022
|
A Learning Approach to Shallow Parsing
|
cs.LG cs.CL
|
A SNoW based learning approach to shallow parsing tasks is presented and
studied experimentally. The approach learns to identify syntactic patterns by
combining simple predictors to produce a coherent inference. Two instantiations
of this approach are studied and experimental results for Noun-Phrases (NP) and
Subject-Verb (SV) phrases that compare favorably with the best published
results are presented. In doing that, we compare two ways of modeling the
problem of learning to recognize patterns and suggest that shallow parsing
patterns are better learned using open/close predictors than using
inside/outside predictors.
|
cs/0008023
|
Selectional Restrictions in HPSG
|
cs.CL
|
Selectional restrictions are semantic sortal constraints imposed on the
participants of linguistic constructions to capture contextually-dependent
constraints on interpretation. Despite their limitations, selectional
restrictions have proven very useful in natural language applications, where
they have been used frequently in word sense disambiguation, syntactic
disambiguation, and anaphora resolution. Given their practical value, we
explore two methods to incorporate selectional restrictions in the HPSG theory,
assuming that the reader is familiar with HPSG. The first method employs HPSG's
Background feature and a constraint-satisfaction component pipe-lined after the
parser. The second method uses subsorts of referential indices, and blocks
readings that violate selectional restrictions during parsing. While
theoretically less satisfactory, we have found the second method particularly
useful in the development of practical systems.
|
cs/0008024
|
Estimation of Stochastic Attribute-Value Grammars using an Informative
Sample
|
cs.CL
|
We argue that some of the computational complexity associated with estimation
of stochastic attribute-value grammars can be reduced by training upon an
informative subset of the full training set. Results using the parsed Wall
Street Journal corpus show that in some circumstances, it is possible to obtain
better estimation results using an informative sample than when training upon
all the available material. Further experimentation demonstrates that with
unlexicalised models, a Gaussian Prior can reduce overfitting. However, when
models are lexicalised and contain overlapping features, overfitting does not
seem to be a problem, and a Gaussian Prior makes minimal difference to
performance. Our approach is applicable for situations when there are an
infeasibly large number of parses in the training set, or else for when
recovery of these parses from a packed representation is itself computationally
expensive.
|
cs/0008026
|
Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon
construction
|
cs.CL
|
Generating semantic lexicons semi-automatically could be a great time saver,
relative to creating them by hand. In this paper, we present an algorithm for
extracting potential entries for a category from an on-line corpus, based upon
a small set of exemplars. Our algorithm finds more correct terms and fewer
incorrect ones than previous work in this area. Additionally, the entries that
are generated potentially provide broader coverage of the category than would
occur to an individual coding them by hand. Our algorithm finds many terms not
included within Wordnet (many more than previous algorithms), and could be
viewed as an ``enhancer'' of existing broad-coverage resources.
|
cs/0008027
|
Measuring efficiency in high-accuracy, broad-coverage statistical
parsing
|
cs.CL
|
Very little attention has been paid to the comparison of efficiency between
high accuracy statistical parsers. This paper proposes one machine-independent
metric that is general enough to allow comparisons across very different
parsing architectures. This metric, which we call ``events considered'',
measures the number of ``events'', however they are defined for a particular
parser, for which a probability must be calculated, in order to find the parse.
It is applicable to single-pass or multi-stage parsers. We discuss the
advantages of the metric, and demonstrate its usefulness by using it to compare
two parsers which differ in several fundamental ways.
|
cs/0008028
|
Estimators for Stochastic ``Unification-Based'' Grammars
|
cs.CL
|
Log-linear models provide a statistically sound framework for Stochastic
``Unification-Based'' Grammars (SUBGs) and stochastic versions of other kinds
of grammars. We describe two computationally-tractable ways of estimating the
parameters of such grammars from a training corpus of syntactic analyses, and
apply these to estimate a stochastic version of Lexical-Functional Grammar.
|
cs/0008029
|
Exploiting auxiliary distributions in stochastic unification-based
grammars
|
cs.CL
|
This paper describes a method for estimating conditional probability
distributions over the parses of ``unification-based'' grammars which can
utilize auxiliary distributions that are estimated by other means. We show how
this can be used to incorporate information about lexical selectional
preferences gathered from other sources into Stochastic ``Unification-based''
Grammars (SUBGs). While we apply this estimator to a Stochastic
Lexical-Functional Grammar, the method is general, and should be applicable to
stochastic versions of HPSGs, categorial grammars and transformational
grammars.
|
cs/0008030
|
Metonymy Interpretation Using X NO Y Examples
|
cs.CL
|
We developed on example-based method of metonymy interpretation. One
advantages of this method is that a hand-built database of metonymy is not
necessary because it instead uses examples in the form ``Noun X no Noun Y (Noun
Y of Noun X).'' Another advantage is that we will be able to interpret
newly-coined metonymic sentences by using a new corpus. We experimented with
metonymy interpretation and obtained a precision rate of 66% when using this
method.
|
cs/0008031
|
Bunsetsu Identification Using Category-Exclusive Rules
|
cs.CL
|
This paper describes two new bunsetsu identification methods using supervised
learning. Since Japanese syntactic analysis is usually done after bunsetsu
identification, bunsetsu identification is important for analyzing Japanese
sentences. In experiments comparing the four previously available
machine-learning methods (decision tree, maximum-entropy method, example-based
approach and decision list) and two new methods using category-exclusive rules,
the new method using the category-exclusive rules with the highest similarity
performed best.
|
cs/0008032
|
Japanese Probabilistic Information Retrieval Using Location and Category
Information
|
cs.CL
|
Robertson's 2-poisson information retrieve model does not use location and
category information. We constructed a framework using location and category
information in a 2-poisson model. We submitted two systems based on this
framework to the IREX contest, Japanese language information retrieval contest
held in Japan in 1999. For precision in the A-judgement measure they scored
0.4926 and 0.4827, the highest values among the 15 teams and 22 systems that
participated in the IREX contest. We describe our systems and the comparative
experiments done when various parameters were changed. These experiments
confirmed the effectiveness of using location and category information.
|
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